plot.ly

Column

Chart A

## Number of items ordered in each aisle (For aisles with items ordered greater than 10000)
insta = instacart %>% 
  group_by(aisle) %>% 
  count() %>% 
  arrange(n) %>% 
  filter(n>=10000) %>% 
  plot_ly(
    x = ~aisle, y = ~n, color = ~aisle,type = "bar") %>% 
  layout(xaxis = list(title = "", tickangle = -45) )
insta

Column

Chart B

## The distribution of order main hour of the day for each day in the weekend for Pink Lady Apples
pink = instacart %>% 
  filter(product_name %in% "Pink Lady Apples") %>% 
  mutate(order_dow=as.factor(order_dow)) %>% 
  group_by(order_dow) %>% 
  mutate(name = "Pink Lady Apples")%>% 
  plot_ly(y = ~order_hour_of_day, x = ~order_dow, color=~order_dow, type = "box")
pink

Chart C

## Comparison between Coffee Ice Crean and Pink Lady Apples in mean order time for each day of the week
pink_mean = instacart %>% 
  filter(product_name %in% "Pink Lady Apples") %>% 
  group_by(order_dow) %>% 
  summarise(mean_hour = mean(order_hour_of_day) ) %>% 
  mutate(name = "Pink Lady Apples")
coffee_mean = instacart %>% 
  filter(product_name %in% "Coffee Ice Cream") %>% 
  group_by(order_dow) %>% 
  summarise(mean_hour = mean(order_hour_of_day) )%>% 
  mutate(name = "Coffee Ice Cream")
com=full_join(pink_mean,coffee_mean) %>% 
  mutate(order_dow=as.factor(order_dow)) %>% 
  plot_ly(
    x = ~order_dow, y = ~mean_hour, color=~name, type = "scatter", mode = "line",alpha = 0.5)

## Joining, by = c("order_dow", "mean_hour", "name")

com